SLAM

SLAM预备知识

"好察非明，能察能不察之谓明；必胜非勇，能胜能不胜之谓勇。"

Posted by Bobin on March 2, 2017

视觉slam

先说视觉这块，首先射影几何的一些内容相机模型，单视几何，双视几何和多视几何。这些内容可以在http://www.robots.ox.ac.uk/~vgg/hzbook/这本书中找到。英文版的，另外中科院的吴福朝编著的“计算机视觉中的数学方法”也很好，他涵盖了上述了MVG in CV book中的大部分内容，强烈安利。

然后是一些视觉特征，这方面就是一些特征，描述子，匹配相关等。见SIFT，ORB、BRISK、SURF等文章。

数学方面首先是三维空间的刚体运动，参考机器人学， 关于优化，SLAM中的优化方法十分基本，参考高斯牛顿，LM，结合稀疏线性代数，其实用的时候会使用一种g2o的图优化库或者ceres。

最难的应该算是李群和李代数，这方面可以参考book state estimation for Robotics。当然不想看书的话可以参考博客http://www.cnblogs.com/gaoxiang12/tag/%E6%9D%8E%E4%BB%A3%E6%95%B0/。

为了看论文的时候能够比较流畅，还应该具备一些概率论的知识，这里推荐bookProbabilistic Robotics pdf

话说高翔博士近期完成一本SLAM的入门book，有理论有实践，写的不错，推荐包含了上述在视觉slam需要的所有基础知识，真是造福大众啊。详细研读此书，以后读各种论文就不会显得那么吃力了吧。最后列举一些玩slam的一些必备工具和相关资源。

tools

  1. ubuntu, install, cmake, bash, vim, qt(optional).
  2. OpenCV install, read the opencv reference manual and tutorial
  3. ros, install, [tutorial}(http://wiki.ros.org/ROS/Tutorials).
  4. python. 可以使用pycharm,作为IDE.

为什么使用ubuntu？因为大家的代码，全是用linux，而且很多使用ros的，ros一定是要Linux的，同时还要cmake。Ubuntu是比较适合初学Linux的人，非常好用

somethind about Calibration

  1. opencv camera Calibration
  2. matlab camera Calibration toolbox
  3. svo camera Calibration
  4. ros wiki camera Calibration

为什么要标定相机呢，因为slam的模型中假设 相机的内参数是已知的，因此有了这个内参数我们才能正确的初始化slam系统。

ROS

ros usually used pakcage

  1. svo
  2. orb slam
  3. ar_tracker_alvar githun page ros page
  4. ros ptam,原始代码不支持ros, 这里给出ros版本的代码. 原始代码网站
  5. DSO

ros books

  1. Learning ROS for Robotics Programming
  2. 机器人操作系统（ROS）浅析

    some blogs about ros

  3. http://www.guyuehome.com/page/1

SLAM基础学习

  1. Multiple View Geometry in Computer Vision。这本书基本涵盖了Vision-based SLAM这个领域的全部理论基础！读多少遍都不算多！另外建议配合Berkeley的课件学习。（更新：这本书书后附录也可以一并读完，包括附带bundle adjustment最基本的levenberg marquardt方法，newton方法等）．
  2. Sparse Matrix，这是大型稀疏矩阵处理的一般办法。可以参考Dr. Tim Davis的课件：Tim Davis ，他的主页里有全部的课程视频和Project。针对SLAM问题，最常用的least square算法是Sparse Levenberg Marquardt algorithm，这里有一份开源的代码以及具体实现的paper：Sparse Non-Linear Least Squares in C/C++
  3. openSLAM
  4. dataset tum
  5. PCL
  6. opencv

推荐阅读的书

  1. Multiple View Geometry in Computer Vision
  2. Probabilistic Robotics pdf
  3. state estimation for Robotics
  4. Quaternion kinematics for the error-state KF
  5. 凸优化，https://web.stanford.edu/~boyd/cvxbook/bv_cvxbook.pdf
  6. 线性系统理论，https://www.amazon.com/Linear-System-Electrical-Computer-Engineering/dp/0199959579
  7. An Invitation to 3-D Vision，https://www.eecis.udel.edu/~cer/arv/readings/old_mkss.pdf
  8. Modern Control Systems，https://www.amazon.com/Modern-Control-Systems-12th-Richard/dp/0136024580
  9. Rigid Body Dynamics，http://authors.library.caltech.edu/25023/1/Housner-HudsonDyn80.pdf。说实话刚体动力学理论我没有找到特别好的书。但是刚体动力学理论很重要。
  10. Feedback Systems: An Introduction for Scientists and Engineers，FBSwiki
  11. 《机器学习》，周志华老师的书。
  12. 线性估计，https://www.amazon.com/Linear-Estimation-Thomas-Kailath/dp/0130224642

vision Navigation

  • Georg Klein and David Murray, “Parallel Tracking and Mapping for Small AR Workspaces”, In Proc. International Symposium on Mixed and Augmented Reality (ISMAR’07, Nara).
  • D. Scaramuzza, F. Fraundorfer, “Visual Odometry: Part I - The First 30 Years and Fundamentals IEEE Robotics and Automation Magazine”, Volume 18, issue 4, 2011.
  • F. Fraundorfer and D. Scaramuzza, “Visual Odometry : Part II: Matching, Robustness, Optimization, and Applications,” in IEEE Robotics & Automation Magazine, vol. 19, no. 2, pp. 78-90, June 2012. doi: 10.1109/MRA.2012.2182810
  • A Kalman Filter-Based Algorithm for IMU-Camera Calibration Observability Analysis and Performance Evaluation
  • SVO- Fast Semi-Direct Monocular Visual Odometry
  • eth zasl sensor,
    • Stephan Weiss. Vision Based Navigation for Micro Helicopters PhD Thesis, 2012 pdf
    • Stephan Weiss, Markus W. Achtelik, Margarita Chli and Roland Siegwart. Versatile Distributed Pose Estimation and Sensor Self-Calibration for Autonomous MAVs. in IEEE International Conference on Robotics and Automation (ICRA), 2012. pdf
    • Stephan Weiss, Davide Scaramuzza and Roland Siegwart, Monocular-SLAM–based navigation for autonomous micro helicopters in GPS-denied environments, Journal of Field Robotics (JFR), Vol. 28, No. 6, 2011, 854-874. pdf
    • Stephan Weiss and Roland Siegwart. Real-Time Metric State Estimation for Modular Vision-Inertial Systems. in IEEE International Conference on Robotics and Automation (ICRA), 2011. pdf
    • Simon Lynen, Markus Achtelik, Stephan Weiss, Margarita Chli and Roland Siegwart, A Robust and Modular Multi-Sensor Fusion Approach Applied to MAV Navigation. in Proc. of the IEEE/RSJ Conference on - - Intelligent Robots and Systems (IROS), 2013. pdf
  • [orb slam]
    • Raúl Mur-Artal, J. M. M. Montiel and Juan D. Tardós. ORB-SLAM: A Versatile and Accurate Monocular SLAM System. IEEE Transactions on Robotics, vol. 31, no. 5, pp. 1147-1163, 2015. (2015 IEEE Transactions on Robotics Best Paper Award). PDF.
    • Dorian Gálvez-López and Juan D. Tardós. Bags of Binary Words for Fast Place Recognition in Image Sequences. IEEE Transactions on Robotics, vol. 28, no. 5, pp. 1188-1197, 2012.

参考

  1. 大疆的YY硕https://www.zhihu.com/question/24492974/answer/29987148
  2. https://zhuanlan.zhihu.com/p/22266788
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